Transcribing voiced musical notes for creating, practicing and sharing of musical harmonies

ABSTRACT

Method for transcription of voiced musical note includes segmenting an electronic audio signal into a plurality of musical note segments, and sampling each note segment to obtain a plurality of audio samples. For each audio sample, an autocorrelation is computed to determine a probability value associated with certain audio frequencies contained within the audio sample. Local maxima are identified in the energy associated with one or more audio frequencies comprising each audio sample and a corrective function applied to reduce the occurrence of octave errors. A true pitch of each note segment is then determined by converting the pitch identification problem to one involving a shortest path through a graph or network of node. Edge weights are computed for a plurality of adjacent nodes i, j comprising the graph, where each node represents one of the musical note segments.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to pending provisional U.S. PatentApplication Ser. No. 62/518,433 filed on Jun. 12, 2017 which is herebyincorporated by reference in its entirety.

BACKGROUND Statement of the Technical Field

This disclosure concerns automated methods and devices which facilitatethe creating, practicing and sharing of music, and more particularlyconcerns methods for transcribing voiced musical notes.

Description of the Related Art

In the music field, harmony involves a combination of concurrentlysounded musical notes which produce a pleasant listening effect. In thisregard, harmony is generally understood to require at least two separatetones or voices to be sounded simultaneously. For example, a simple formof harmony would involve a second note sounded at a pitch that is doublethe pitch of a basic melody. A Third harmony is one in which the harmonypart is a musical Third above the original pitches comprising themelody. Most commonly, a musical harmony will comprise between three andsix voice parts or tones.

In theory, various harmony parts (e.g. vocal harmony parts) could becreated separately and then combined to create a song. But in order tocreate vocal harmonies that have a pleasing sound, vocalists will oftenwork together with producers. For example, this process can take placein a recording studio with the various vocalists present so that theycan practice with one another and hear how the combined vocal parts willsound when combined together.

SUMMARY

This document concerns a method for accurate transcription of voicedmusical notes. A microphone is used to convert sound waves comprised ofmonophonic audio produced by a voiced rendition of the musicalcomposition, into an electronic audio signal. The electronic audiosignal is then stored in a data storage device. An electronic processingdevice receives certain manual user inputs which specify timinginformation for the musical composition to be transcribed. For example,the user specified timing information can be selected from the groupconsisting of a music time signature and a tempo associated with themusical composition. The electronic audio signal is then processed inthe electronic processing device to determine a true pitch of eachmusical note of the voiced rendition.

The true pitch is determined in the electronic processing device througha series of steps which can begin by segmenting the electronic audiosignal into a plurality of musical note segments. For example, in somescenarios each of the musical note segments can be selected to comprisean eighth note. Each note segment is sampled to obtain a plurality ofaudio samples. According to one aspect, the audio samples arepreferentially selected from portions of the note segment which excludeleading and trailing edges of the note segment

For each audio sample, an autocorrelation is computed to determine aprobability value associated with certain audio frequencies containedwithin the audio sample. The resulting plot of probability values willvary in magnitude over a frequency range associated with audio producedby a human voice. Within such frequency range there will be one or moreinstances involving the occurrence of local maxima or peaks in theenergy associated with one or more audio frequencies comprising eachaudio sample. A corrective function can be applied to help eliminate oneor more of the local maxima which are determined to be associated withoctave errors. After octave errors are accounted for in this way foreach audio sample, the audio frequency associated with each of the oneor more local maxima can be quantized to a nearest musical note on thewestern musical scale.

A true pitch of each note segment is then determined by converting thepitch identification problem to one involving a shortest path through agraph or network of nodes, where each node corresponds to a possiblepitch of the corresponding note segment. For purposes of such a network,a virtual note or pitch can be included to represent periods of silence.In a shortest path solution, it is necessary to compute edge weightsbetween nodes. According to one aspect of the solution presented herein,edge weights are computed for a plurality of adjacent nodes i, jcomprising the graph, where each node represents one of the musical notesegments.

The edge weights between any two adjacent nodes i, j are computed byaveraging the negative log likelihood of the probability values whichare determined for the audio frequency associated with each musicalnote, a determined over the range of samples which are associated witheach particular audio segment. It will be appreciated that theseprobability values which are averaged are based on the strength of thelocal maxima at such frequencies as determined in connection with eachaudio sample. Each resulting edge weight assigned between adjacent nodesi and j is then a negative log likelihood of the probability that a nextmusical note segment has a pitch associated with node j, assuming thatnode i was selected. Based on this shortest path analysis, a true pitchto be assigned each of the musical note segments is determined.

According to one aspect, the corrective function reduces the occurrenceof octave errors by giving preference to a first audio frequencyassociated with a first local maxima having the lowest audio frequencyof the one or more local maxima. This preference being conditional onthe others of the one or more local maxima associated with the sameaudio sample having a probability value magnitude within a predeterminedthreshold range of the first local maxima. In some scenarios, thecorrective function applied for this purpose is the integral of aKumaraswamy distribution.

The process of determining the true pitch of each note can furtherinvolve applying a regularization function when assigning the edgeweights to minimize a possibility that an oscillating pitch will (e.g.,an oscillating pitch associated with vibrato singing) will significantlyaffect assigned edge weights. In some scenarios, the regularizationfunction can be comprised of a Laplace distribution.

After the pitch of the musical note segments are determined, the processcan continue by selectively concatenating contiguous groups of themusical note segments which are determined to have the same pitch, intonotes of longer duration. The resulting transcription data whichspecifies a true pitch of each of the musical note in the voicedrendition can then be stored in a data storage device.

In some scenarios, the voiced rendition can comprise a first musicalharmony part of the musical composition, and the electronic processingdevice displays on a note grid the musical notes which correspond to thetrue pitch which has been determined. Further, the musical compositioncan be comprised of at least a second musical harmony part. In such ascenario, the electronic processing device can further display on thenote grid, concurrent with the musical notes comprising the firstmusical harmony part, those musical notes which are associated with atleast the second musical harmony part.

The process can also include determining in real-time a pitch of asecond voiced rendition of the first musical harmony part. An audioanalysis process applied for the real-time determining of the pitch ofthe second voiced rendition is advantageously selected so that it isdifferent from the audio analysis process applied for determining thetrue pitch of the voiced rendition. A pitch indicator can be displayedin conjunction with the note grid to indicate whether musical notes ofthe second voiced rendition match a specified pitch and timing of themusical notes of the first musical harmony part. Further the secondmusical harmony part can be caused by the electronic processing deviceto be audibly played through a loudspeaker concurrent with determiningthe pitch of the second voiced rendition of the first harmony part.

BRIEF DESCRIPTION OF THE DRAWINGS

The solution will be described with reference to the following drawingfigures, in which like numerals represent like items throughout thefigures, and in which:

FIG. 1 is a conceptual diagram of a computer system that is useful forcollaboratively creating, sharing and practicing musical harmonies.

FIGS. 2A-2F comprise a set of flow charts that are useful forunderstanding a method for creating, sharing and practicing musicalharmonies.

FIG. 3 is a drawing which is useful for understanding a home screenthrough which a user can interact with a user equipment to facilitatecollaborative creation, sharing and practicing of musical harmonies.

FIGS. 4A-4E are a series of drawings that are useful for understandinghow a user can interact with the user equipment to facilitate a lessonsession.

FIGS. 5A-5E are a series of drawings that are useful for understandinghow a user can interact with the user equipment to facilitate creationof a new song.

FIGS. 6A-6B are a series of drawings that are useful for understandinghow a user can interact with the user equipment to facilitate editing ofan existing harmony track.

FIG. 7 is a flowchart that is useful for understanding a real-timeprocess for pitch detection.

FIG. 8A is a histogram that is useful for understanding certain aspectsof the real-time pitch detection process.

FIG. 8B is a plot which is useful for understanding power variationsamong bins associated with a particular audio sample.

FIGS. 9A-9B are a series of drawings which are useful for understandinga flow of a note transcription process.

FIG. 10 is a graph which is useful for understanding certain aspects ofa note transcription process described herein.

FIG. 11 is plot which is useful for understanding how frequencyprobabilities can be determined for short segments of an audio track.

FIG. 12 is a block diagram of an exemplary computer system that canperform certain processing operations as described herein.

DETAILED DESCRIPTION

It will be readily understood that the solution described herein andillustrated in the appended figures could involve a wide variety ofdifferent configurations. Thus, the following more detailed description,as represented in the figures, is not intended to limit the scope of thepresent disclosure, but is merely representative of certainimplementations in various different scenarios. While the variousaspects are presented in the drawings, the drawings are not necessarilydrawn to scale unless specifically indicated.

A solution disclosed herein concerns a Music Harmony Tool (MHT) andmethod of using same. For a performer who seeks to learn, practice,share and/or collaboratively create harmonies there are a number ofchallenges which must be overcome. A harmony can involve a plurality ofdifferent parts in which performers are singing different notes at thesame time. A singer seeking to create, learn or practice a harmony partmust focus on the specific notes required for their part of the harmonywithout being distracted by other parts of the harmony. Further,different performers necessary for creating a harmony may be unavailableat the same time, may be physically separated by large distances and/ormay have different skill levels requiring different amounts of practicetime. Not all participants may want to participate in all practicesessions. But for singers with less experience, it can be difficult tomaster a particular harmony without the presence of others, and aninexperienced singer may not be able to tell when the harmony part theyare singing is being performed properly. Accordingly, an MHT disclosedherein provides certain advantages for learning creating, practicing andcollaborating in regards to musical harmonies.

Referring now to FIG. 1 it can be observed that an MHT system can insome scenarios include an application server 102 which has access touser account data store 103, and a main data store 105. One or moreclients comprising user equipment (UE) computer systems 106 ₁-106 _(n)can communicate with the application server using a computer datanetwork 108. The UE computer systems 106 ₁-106 _(n) can comprise anysuitable type of computing device that is capable of carrying out themethods and functions described herein. In some scenarios, the userequipment can comprise a desktop computer with suitable networkinterface connections to carry out certain data communication operationsas described herein. In other scenarios, the user equipment can comprisea portable data communication device such as a smart phone, a tabletcomputer, or a laptop computer. Other types of computing systems whichcan be used for this purpose include dedicated computing devices whichare designed to exclusively carry out the methodologies and functionsdescribed herein. Although a network-based arrangement is presentedherein, it should be understood that several aspects of the solution canalso be implemented in a non-networked computer system. These variousaspects and features are described below in greater detail

The application server 102 can comprise a computer program andassociated computer hardware that provides MHT services to the UEcomputer systems 106 ₁-106 _(n) to assist in carrying out one or more ofthe methods and functions described herein. The user account data store103 can contain certain user account data 114 pertaining to individualusers who have established user accounts to facilitate access and use ofthe MHT. In some embodiments, the user account data store 103 cancomprise user account data such as passwords, email addresses, practicesession scores reflecting user proficiency, and so on. The user accountdata 114 can also include other types of user authentication data,digital certificates, and/or a transaction log. The main data store 105can comprise music data files 110 ₁, 110 ₂ , . . . 110 _(n) associatedwith one or more songs. As described below in greater detail, each ofthe music data files 110 ₁, 110 ₂ , . . . 110 _(n) can include digitaldata representative of one or more harmony parts created by one or moreusers.

The computer data network 108 is a comprised of a data communicationnetwork suitable to facilitate communication of data files, user dataand other types of information necessary to implement the MHT and MHTservices described herein. Computer network 108 can also facilitatesharing with UE computer systems 106 ₁-106 _(n) certain computingresources that are available at application server 102. Exemplarynetworks which can be used for this purpose can include packet datanetworks operating in accordance with any communication protocol nowknown, or known in the future. The organizational scope of such networkcan include but is not limited to one or more of an intranet, anextranet, and the Internet.

FIGS. 2A-2F comprise a set of flow charts that are useful forunderstanding a method for creating, sharing and practicing musicalharmonies. FIG. 2A shows that an MHT process 200 can begin at 202 andcontinue at 204 where a graphical user interface (GUI) comprising a homescreen is displayed on a display screen of a UE (e.g. any one of UE 106₁-106 _(n)). An example of a home screen 350 facilitated by a UE 106₁-106 _(n) is shown in FIG. 3. The home screen 350 includes exemplaryuser selectable control elements 352, 354, 356, 358 which allow users toselect various MHT functions such as “Rehearsal”, “Lessons”, “BuildHarmonies” and “Create New Song”. At least one other user selectablecontrol element (e.g., control element 360) can facilitate user accessto certain user data such as user account data 114. In some scenarios,the UE home screen can be presented on a display screen of a UE 106₁-106 _(n). According to one aspect, the display screen can be a touchscreen display though which a user can select available functions bytouching one or more of the selectable control elements. In otherscenarios, a computer pointing device, keyboard or other type of userinterface can be provided for this purpose.

From the home screen a UE 106 ₁-106 _(n) receive one or more user inputselections 206. The process continues in steps 208, 212, 216 and 220where a determination is made as to whether the user has selected aparticular option. For example, selections can include a “Lessons”session option 208, a Rehearsal session option 212, or a Build Harmonysession option 216. A user can also select a terminate session option220. Depending on the user selection, the UE will transition to displaythe Lessons GUI at 210, a Rehearsal GUI at 214, or a Build Harmony GUIat 218 to begin the selected session as shown.

If a lesson session option is selected at 208, then the processcontinues at 224 in FIG. 2B. At 224, the UE receives a user inputselection to begin a practice session with respect to a particularsegment of a musical selection or song. The user's choice of aparticular song can be entered in a song selection screen 400 a as shownin FIG. 4A. In response to a user selection of a particular song (e.g.,Song 3), the UE 106 ₁ . . . 106 _(n) can retrieve certain datacomprising a particular segment of a musical selection or song. Forexample, in some scenarios the data comprising a segment of a particularmusical selection or song 110 ₁-110 _(n) can be retrieved from the maindata store 105 in response to a request from a UE directed to theapplication server 102. In some scenarios, the segment can comprise theentire musical selection or song. In other scenarios the segment cancomprise a portion of the musical selection.

Once the particular song has been selected, the process continues on to225 where the user can select a particular harmony part that he wishesto sing. For example, in a scenario shown in FIG. 4B the user can selectthe part of Soprano, Alto, Tenor or Bass from a part selection screen400 b. In some scenarios, the text associated with each identified partcan be visually coded in accordance with a pattern or color. Once thesystem receives the user selection of a particular part that the userwishes to practice, the process continues on to 226 where selected songis loaded in preparation for the practice session. This condition isillustrated in FIG. 4C which involves a graphic display 400 c of amusical note scale in which particular musical note legends orannotations 410 are aligned along a vertical note axis 411 of thedisplay.

In FIG. 4C, each musical note block 404, 405, 406, 407 is visually codedby means of a selected visual indicator such as a cross-hatching patternor color. In some scenarios, the pattern or color of the musical noteblocks associated with a particular harmony part can be chosen tocorrespond with a visual coding (e.g., color or pattern) of one of thevarious harmony parts listed in FIG. 4B. For example, if text specifyingthe “Soprano” part is presented in blue colored text in FIG. 4B, thenthe musical note blocks 404 which correspond to the Soprano part in FIG.4C can be presented in the same color blue. If text specifying the“Alto” part is presented in red colored text in FIG. 4B, then themusical note blocks 405 which correspond to the Alto part in FIG. 4C canbe presented in the same color red. Consequently, the user can moreeasily identify through such visual coding which notes are associatedwith each part.

It can be observed in FIG. 4C that the musical note blocks 407 whichcorrespond to the particular harmony part selected by the user can behighlighted or otherwise marked to facilitate identification. Thesemarked musical note blocks are sometimes referred to herein as targetmusical note blocks. Once the practice session begins, the user isintended to sing these highlighted or visually marked target musicalnote blocks.

It can be observed in FIG. 4C that each musical note block 404, 405,406, 407 is vertically aligned with the appropriate annotated note textin note legend 410. The musical note blocks are also aligned along thetime axis 412 with a particular time during the particular musicalsegment of the selected song. For example, in FIG. 4C target musicalnote block 407 aligned with a particular time within a song to indicatethe note that is required at a particular time for that particularharmony part. The duration of each note in the musical harmony isindicated by the width W of each note block along a direction alignedwith time axis 412. Time scale is marked by vertical lines 408, 409which extend transverse to the time axis. For example, lines 408 canindicate musical measures or bars, and lines 409 indicate beats withineach measure. A current time axis 403 can specify the point in timewhere the user is intended to begin singing.

When the practice session actually begins, the measures and musical noteblocks 404, 405, 406, 407 will automatically begin to move or scroll(e.g., from left to right) on the screen in accordance with the musictempo and the passage of time. In this regard, the musical note blockscan be said to scroll in a direction aligned with the time axis. Moregenerally, the user's view into the note grid can be understood as beingallowed to slide as the song progresses so that a “window” appears tothe user to scroll along the time axis 412. The resulting note gridwhich is displayed can be advantageously centered in the display on thecurrent time axis 403 within a particular song the user is practicing.This process is described below in greater detail. Accordingly,

The practice session actually begins at 227 and 228 with the UE audiblyreproducing the music segment, and also scrolling the music note blockson the display screen. The scrolled notes will include the targetmusical note blocks 407 which the user is expected to sing with respectto the reproduced segment of music. FIG. 4D shows a display screen 400 dcorresponding to steps 227 and 228. Optionally, the process can furthercomprise displaying 230 to the user certain information or textualguidance (not shown in FIG. 4D) explaining how the particular segment isto be performed or sung.

At 232 a user will sing notes having a pitch in accordance with thetarget musical note blocks 408, while observing the target musical noteblocks 408 which are scrolled on screen on screen with the passage oftime. Using the contextual information provided in steps 227, 228 and230, the user attempts while singing to match the pitch of their voiceto the note that is specified by the target musical note blocks. As theuser sings, the UE captures the resulting sound associated with suchsinging with a microphone so as to generate an audio signal. The UEfurther performs certain processing operations at 234 to analyze thepitch of the musical notes that are sung. The results of such analysisare displayed on screen at 236 in the form of visual cues 414 a, 414 bso that the user can understand how well the singing performance pitchis matching the specified target notes. These visual cues are presentedor displayed to the user. In some scenarios, the visual cues can bepresented in the form of micro-notes, having a duration which issubstantially less than a duration of an entire measure. For example, insome scenarios each micro-note can be one musical beat or less.Micro-notes are selected to have a relatively short duration relative toeach measure so as to facilitate continually updating the informationpresented to the user regarding the pitch that they are singing. It canbe observed in FIG. 4D that the visual cues 414 a, 414 b are displayedin real time at a location along the time axis 412 to indicate actualtiming of sung notes. Of course other types of visual cues are alsopossible and it should be appreciated that any such alternativearrangement of visual cue can also be acceptable for purposes of thesolution presented herein.

FIG. 4D shows how the time-aligned visual cues are generated anddisplayed in real time as the user sings. In the example shown, visualcues 414 a have the correct pitch corresponding to target musical noteblocks 407. As such, visual cues 414 a are displayed directly overlaidon the target musical note blocks 407. Conversely, visual cues 414 bhave the incorrect pitch as compared to the target musical note blocks407 and therefore appear above or below the target musical note blocks407 to indicate that the sung pitch is too high or too low.

In the scenario shown in FIG. 4D, notes 407 that are about to be sungenter from the right side of the screen and are intended to be voiced bythe user as they scroll past the current time axis 403. The notes thendepart the screen to the left. Visual cues regarding a timing of theuser's singing can be provided by observing how well the leading andtrailing edges of a particular visual cue 414 a, 414 b align with theposition of the target musical note blocks 407 along the time axis 412.For example, in FIG. 4D a timing misalignment “t” between the leadingedge of a target musical note block 407 and the leading edge of a visualcue 414 a indicates that the user was late in beginning to sing theparticular note. The foregoing visual cues can be visually observed andcompared by a user in real-time to target musical note blocks 407

The visual cues shown in FIG. 4D are useful for conveying informationconcerning the timing and pitch of musical notes to be voiced by theuser. In some scenarios, it can be desirable to modify the visual cuesto also convey information concerning the relative loudness of theharmony part that is to be sung. This additional information can bevisually conveyed in any suitable manner. For example, in some scenariosthe information can be conveyed by adjusting the “thickness/height” ofthe target musical note blocks 407 in the vertical direction (i.e., indirections aligned with the vertical note axis 411). Thicker/tallernotes would indicate notes to be sung more loudly (forte), andnarrower/skinnier notes would indicate quieter (piano) sections. Ofcourse other variations are also possible. For example, the saturation,brightness or intensity of the displayed note blocks could be varied toindicated differences in how loud the musical note blocks are to bevoiced. In other scenarios, the color of the musical note blocks can bevaried.

The visual feedback provided to a user by the UE during practice givesreal-time input to the singer. This feature greatly helps with trainingand improving vocals. For example, it eliminates the need for a user torecord an entire track then go back and listen to it afterward.Accordingly, a user can avoid the slow and tedious feedback processcommon to conventional methods.

At 238 a determination is made as to whether the lesson corresponding tothe particular segment is completed. If not (238: No) then the processreturns to step 232 where the user continues to sing and receivefeedback from the UE. If the lesson is completed with respect to theparticular segment (238: Yes) then the process continues on to 240 wherethe UE can calculate and display a score with respect to the particularpractice session. In some scenarios, this score can be used to helpguide the user toward appropriate exercises or further practice sessionsthat are automatically selected to help the user improve hisperformance. For example, in some scenarios, the score provided to theuser can be segmented to reveal performance associated with differentportions of a harmony. In such a scenario, the user can be directed tothose areas where the users performance is less than satisfactory. Inother scenarios, where the user's score reveals a more generalizedweakness in their singing ability, then the system can select musicalsinging exercises which are particularly tailored to improve certainsinging skills.

At 242, the user can select whether to proceed with the next lessor ormusic segment. If so (242: Yes) then the process returns to 224 forpracticing the next segment. Otherwise the process can terminate orreturn to the home screen at 244.

Referring once again to FIG. 2A, If a rehearsal session option isselected at 212, then the process continues at 246 in FIG. 2C. At 246the UE receives a user input selection to load a Song Library. Forexample, this song library can be requested from an application server102. Information concerning the selected Song Library can be displayedto the user in a screen similar to screen 400 a as shown in FIG. 4A.Once the Song Library is loaded, the user can interact with the GUI tosubsequently select at 248 a particular song for rehearsal.

The user can optionally begin the rehearsal by selecting a particularharmony part (first harmony part) of the song which has been loaded sothat it can be played out loud using a UE loudspeaker. Accordingly, theprocess can continue at 250 with the UE receiving a user inputspecifying a particular harmony part which is to be played out loud.Shown in FIG. 2E is a display screen illustrating how the user caninteract with the UE to facilitate such playback. Here, the user haspreviously selected Song 3 from the music library and has selected toplay out loud the Soprano part. The UE can receive a user input throughthe UE by the user selecting the “Play” command 424 which causes the UEto play the user selected harmony parts. Consequently, the first harmonypart which has been selected can be heard by the user during therehearsal session. A volume or amplitude of the playback can becontrolled such as by a slider or other type of control element 420.

The user can further indicate a particular harmony part (second harmonypart) of the song which the user wishes to actually practice.Accordingly, the process can continue at 252 with the UE receiving auser input specifying a particular harmony part which is to bepracticed. To facilitate the rehearsal or practice session, the UE canreceive from the user through the GUI one or more selections to controla playback volume 254 of certain harmony parts of the song (e.g. tocontrol the playback volume of a first harmony part while the userrehearses practices singing the second harmony part). In such ascenario, it is advantageous for the first harmony part to be playedback to the user by means of headphones while the user performs thesecond harmony part. Such an arrangement allows the UE to separatelycapture the second harmony part performed by the user without audiointerference caused by playback of the first harmony part. In somescenarios, the user can select a plurality of different harmony partswhich are to be played back while the user rehearses or sings a selectedharmony part. A volume or amplitude of the playback can be controlled bya slider or other type of control element 420.

The UE can be caused to play a certain harmony part as described hereinby selecting the “Play” command 424. The UE will respond to such inputby playing the user selected harmony parts (e.g. while the user sings adifferent selected harmony part for practice/rehearsal purposes).Alternatively, the UE can be caused to concurrently play the userselected harmony parts and record a different harmony part, which isconcurrently sung by the user. This functionality can be initiated whenthe UE receives a user input selecting the “Record” control 426. Ineither scenario, the UE will display visual cues (e.g. highlightedtarget musical note blocks 428) to indicate the harmony part which is tobe sung by the user. Such display can be arranged in a manner similar tothat described herein with respect to the practice screen shown in FIG.4D. More particularly, at 258 the UE visually displays highlightedtarget musical note blocks 428 for the user part while the user singshis/her part. The UE can concurrently also visually display harmonynotes 422 for the harmony parts other than user part as those otherparts are being audibly played back by the UE

As the rehearsal process progresses, the UE can advantageously displayat 260 real-time visual feedback to the user on a display screen of theUE. Such visual feedback can be similar to the feedback described hereinwith respect to FIG. 4D to indicate how well the user's singingperformance matches a desired harmony part. Visual cues can be providedto indicate how well the user performance matches a desired performancewith respect to one or more of pitch, volume and/or timing with respectto user's selected part.

At 262 a decision can be made as to whether the selected song or segmentis done playing. If not (262: No) then the process continues at 258. Atany point during the session while the song is playing, the UE canreceive through its user interface at 264 a user input to rewind or fastforward through portions of the selected song. Further, at 266 the UEcan receive through its user interface a user input to optionally selectto save the harmony part that has just been recorded during therehearsal session. At 268, the user can select to have the rehearsalprocess terminate at 270 (270: Yes), whereby the UE can terminate orreturn to the home screen. If the process is not terminated (268: No),then the process can continue or return to 246 so the process continues.

An MHT disclosed herein can also facilitate creation of entirely newsongs by the user. The user can record a harmony to begin the processand define that harmony as being part of a new song. Further, the usercan create one or more harmony parts of the newly created song. Thisprocess is best understood with reference to FIG. 2D. More particularly,the UE can receive at 216 a user selection to “Create New Song” at 216.This user selection can be indicated by a user activation of a “CreateNew Song” GUI control 356 in home screen 350. At 218, the processdisplays the “Create New Song” GUI shown in FIG. 5A. Here, the user caninsert a song title, set the beats per minute (BPM) that the new song isintended to have, and select a time signature for the song (e.g., 3/4time or 4/4 time). As shown in FIG. 5B, the user can then proceed to seta title (e.g., “Soprano”) for a particular part that they will becreating.

The process then proceeds to step 272 in FIG. 2D. The system can wait at272 for a user input selection indicating that the user is ready tobegin recording a new harmony. The system determines at 274 whether auser has indicated that they are ready to begin recording a new harmony.If so (274: Yes) then the process continues on to step 276 where the UEuses a microphone and associated audio circuitry to record a harmonypart. If creating a new song, then the harmony part can be any harmonypart the user wishes to create.

As the UE records the harmony part, it can display a preliminaryindication of the pitch of the user's voice as shown in FIG. 5C. Theuser's voice pitch can be presented in real time as the user is singingin the form of micro-notes 502. As shown in FIG. 5C, the micro-notes areof relatively short duration. For example, in some scenarios eachmicro-note 502 can be 1 beat or less in duration. The UE will continuerecording and wait for a user input to stop recording at 278. When suchan input is received (278: Yes) it will serve as an indication that theuser is done creating the harmony part. At this point, the micro-notesare combined by the UE as shown in FIG. 5D to form musical note blocks504. For example, this step can be performed in some scenarios throughthe use of post processing operations performed by the UE after therecording operation has been stopped.

The UE can then offer the user the opportunity to play the part at 280,in which case the newly recorded harmony part will be played at 282. Theuser may be provided with the opportunity at 284 to decide whether thenewly recorded harmony part should be saved. If so (284: Yes) then theprocess continues on to step 296 in FIG. 2E.

At 286 the user can be prompted as to whether they would like to addanother harmony part. If so, the process can return to 272. Otherwisethe user can elect to terminate the Build Harmony section at 288. If so(288: Yes) then the session terminates at 290 or returns to the MHT homescreen.

FIG. 5E illustrates an example of a UE screen display in a scenariowhere the user has elected at 286 to add a further harmony part to thesong. In FIG. 5E the part named “Soprano” has been created as “track 1”and a second part (e.g. “Alto”) can be added by the user as “track 2” byusing the keypad 510. If the user chooses to proceed with the creationof the second part (297: Yes) in FIG. 5E, they can activate a userinterface control 508 to cause the UE to create the second harmony part.The process can then return to step 272 in FIG. 2D.

In some scenarios, the process can continue at 298 where the user can beprompted to indicate whether they wish to add a musical collaborator toparticipate in the creation of one or more harmony parts. If nocollaborator is to be added (298: Yes) the process can terminate at 299or can return to a home screen. If a collaborator is to be invited toparticipate, then the process can continue with step 302 shown in FIG.2F.

At 302 the UE can receive user input from the user specifying an emailaddress, user name, and/or role (e.g. a harmony part) of a potentialcollaborator. Thereafter, at 304 the UE can request and receiveconfirmation that the identified person is to be invited to participateas a collaborator with respect to a specified harmony or song. At 308,the UE can communicate with the application server 102 to determinewhether the identified person is an existing user of the MHT systemdescribed herein for which user account data is available. If so, thenthe application server 102 can cause the person to be notified (e.g. viaemail or text message) at 309. For example, the application server 102can send a message to the identified person that they have been invitedto participate as a collaborator in connection with creation of aparticular song.

In some scenarios, the person invited to collaborate is not an existinguser (308: No), in which case the UE can cause the person to be notified(e.g. via email or text message) that they have been invited toparticipate in creating a song using the MHT tool. For example, suchinvitation can be generated by an application server 102. A response maybe subsequently received from an invitee at 312 (e.g. received at theapplication server). If the response indicates rejection of theinvitation (314: No) then the application server can send a notificationto the UE which initiated the invitation that the collaboration requesthas been declined. However, if the invitation is accepted (314: Yes)then the process continues to 318 where the invited user is requested at318 to download an MHT software component (an application) to theinvitee's UE. Thereafter, the invitee can download the softwarecomponent to their UE and can create an MHT account at 320 bycommunicating account data to the application server 102. Havingdownloaded the required software component and established themselves asa new user of the MHT system, the invited user or collaborator can thenaccept a particular song on which they have been invited to collaborate.The process terminates at 324 and/or can return to a system home screen.

During a process of creating a new song or modifying an existing song itcan sometimes be desirable to edit an existing harmony track. Thisprocess is illustrated in FIGS. 6A and 6B. The process can involvedisplaying a note grid for a particular track that has been recorded.The note grid can be similar to the note grids described herein withrespect to FIGS. 4C-4D and/or FIG. 5D. The user can manipulate ahighlighted vertical note region bar 605. According to one aspect, theUE can be configured using a note size selector control 610 to vary awidth p of the bar, and thereby choose from 16th, 8th, quarter, half, orwhole notes.

In the scenario shown in FIG. 6A, the user has used next and back scrollcontrols 602 a, 602 b to selectively control which portion of a notegrid is displayed. The user has set the note region bar to correspond toa quarter note. In the displayed note grid, the user can control operateuser interface so that the note region bar 605 aligns with a particularnote to be edited.

Once a particular note has been selected, the user can make use of aplurality of available editing controls 608 to perform a user initiatedoperation on the selected note. For example, the available editingcontrol 608 can include a delete control 612 to permit the user todelete the selected note, an add control 614 to allow the user to add anew note, a merge control 616 to allow the merging of two existing notesin the note grid, and a divide control 618 which allows the user todivide a note into two notes of shorter duration.

In the example shown in FIGS. 6A and 6B, the note which the user hasmarked for editing is the third quarter note 604 in a measure 606. Theuser marks this note 604 with note region bar 605 to indicate that note604 has been selected for editing. The user then activates the dividecontrol 618 to split the quarter note 604 from note 622. The userfurther makes use of the user interface to lower the pitch of the notefrom G3 to F3. For example, in a UE equipped with a touch-screen, thisadjustment in pitch to the highlighted or marked note 604 can beaccomplished by using a finger to slide or drag the note 604 tocorrespond to a different note on the scale. The edited harmony trackcan be saved by the UE when the user has completed all necessary edits.

Real-time Pitch Detection

Real-time pitch detection (RTPD) processing in the solution presentedherein is implemented by a novel technique involving evaluation of amonophonic audio signal as produced by an individual human singer.Specifically, it is designed to give accurate feedback to a singer abouttheir actual pitch and amplitude relative to the known “correct pitchand amplitude”. The RTPD processing involves a frequency-domain basedalgorithm using a modified Fast Fourier Transform (FFT). The FFT isoptimized for audio frequencies which are singable by the human voice(approx. 60 Hz-1400 Hz). The process is described below in furtherdetail with reference to FIGS. 7 and 8.

The process begins at 702 and continues at 704 where a constant-Qtransform is applied to detect pitch information of from an audio signalsample or chunk. As is known, a standard FFT transform can be used tofaithfully convert chunks of time-domain data (audio signal) intofrequency-domain data (spectrogram). In a conventional FFT, thisfrequency-domain spectrogram is linear. However, the human eardistinguishes pitch on a pseudo-logarithmic scale. Therefore, instead ofa traditional FFT, the RTPD processing algorithm in the present solutionuses a “constant-Q” transform where “Q” is the ratio of the centerfrequency to the bandwidth of a corresponding logarithmic filter.

The constant-Q transform is well-known in the field of signal processingand therefore will not be described herein in detail. However, it willbe understood that the constant-Q transform is computed by applying aFFT after first applying a logarithmic filter to the underlying data. Asis known, this process can be equivalently implemented as applying theFFT with a logarithmic kernel. See, e.g., Brown, J. C., & Puckette, M.S. (1992). An efficient algorithm for the calculation of a constant Qtransform. The Journal of the Acoustical Society of America, 92(5),2698. http://doi.org/10.1121/1.404385).

In practice, a large number of parameters must be chosen for thisconstant Q transform, and these parameters can be optimized for thespecial case of giving real-time feedback to human singers. For example,an RTPD processing algorithm can advantageously involve the applicationof the following specific parameters, which were identified empiricallyas a result of experiments with audio recordings of professionalsingers:

-   -   FFT bins per octave is set to 48, in order to create sufficient        frequency resolution for feedback purposes while maintaining        real-time performance on modern mobile devices.    -   Frequency to bandwidth ratio of the logarithmic filter is set to        a value between 250 and 300 Hz, which provides a good tradeoff        between filtering spurious microphone/background noise and        precision. For example, in some scenarios, the value can be        chosen to be 275 Hz.    -   Length of the audio signal used is set to 2048 samples (42.7 ms        sample length for 48 kHz sampled audio, or 50 ms sample length        for 41.1 kHz sampled audio), which provides sufficient frequency        resolution for this application without introducing a        significant processing delay before feedback can be provided to        the singer.

As is known, the window size of an FFT that is selected for pitchdetection will affect the frequency resolution. A bin is a spectrumsample of the audio signal, and can be understood as defining thefrequency resolution of the FFT window. Frequency resolution for pitchdetection is improved by increasing the FFT size, i.e., by increasingthe number of bins per octave. However, an excessive number of bins canresult in processing delays that render real-time pitch detectionimpractical. The parameters identified herein have been determined toprovide acceptable resolution of singing pitch while still facilitatingreal-time processing for the benefit of the singer.

With the foregoing parameters applied, an RTPD processing algorithmdetects the current pitch based on the constant Q transform of a shortaudio signal with N samples Q_(A)=Q(a_(0.N)) by detecting the peakfrequencies and generating at 706 a histogram of potential “pitchcandidates”. An example histogram 800 showing the results of thisprocess are illustrated in FIG. 8A. The plot in FIG. 8A shows estimatedpitch frequency versus time. The particular plot shown in FIG. 8A wasobtained by applying the constant-Q transform described herein to audioof a tenor singing eight notes of a major scale. These eight notes startat a low frequency of approximately 220 Hz and end at a high frequencyof approximately 440 Hz.

In histogram of FIG. 8A, each bin 804 is represented as a shadedrectangle. Each column 802 of bins 804 is created from one sample of theaudio signal. For example, in a scenario where the duration of the audiosignal sample is set to 2048 as described above, the audio signal sampleassociated with each bin would have a duration of 42.7 ms (in the caseof 48 kHz sampled audio). Further, it can be observed in FIG. 8A, thateach rectangular bin 804 is shaded to an extent. In the example shown,the darker shaded rectangles represent higher estimated power levels inthat bin. The bins which correspond to the eight major notes which aresung in this example are identified by reference number 806.

The process continues by qualifying the pitch candidates obtained in706. To qualify pitch candidates, the RTPD processing algorithm cansearch those bins which correspond to each particular audio sample. Forexample, this search can proceed from lowest to highest frequency withina predetermined frequency search range to identify locally maximal bins(i.e., bins having a locally maximal power level) which are associatedwith a particular audio sample. Shown in FIG. 8B is a plot which isuseful for understanding this process. FIG. 8B shows how the power levelwhich is associated with bins covering various frequency ranges willvary with respect to frequency for given audio sample or chunk. Thisplot can be understood conceptually with reference to FIG. 8A whichshows that bin power variations along an axis line 812 can be evaluatedamong a set of bins in a particular column that are associated with oneparticular audio sample or chunk. It can be observed in FIG. 8B that aresulting plot of bin power versus frequency will include one or morelocal maxima or local peaks 814, 816, 818 where the power level ofparticular frequency ranges is noticeably of greater magnitude ascompared to other nearby frequencies. Each local maxima or peak willhave a contour or shape which can be mathematically characterized.

Starting from the lowest frequency is useful to ensure that the correctpeak is identified. Partial onsets with lower frequency (e.g., localpeak 814) do not typically have higher energy than the correct pitch(e.g., local peak 816 in this example). In contrast, for the case ofhuman singing, partial onsets at higher frequencies (e.g., local peak818) will sometimes have slightly more energy in them as compared to thecorrect pitch. Accordingly, by scanning the peaks from the lowest tohighest frequency and only accepting pitch candidates which exceed acertain threshold proportional to the current best (i.e., greatestmagnitude) peak (for example at least 110% larger than the current bestpeak), partial onsets with a higher energy can be avoided in most cases.Further, the RTPD processing algorithm is advantageously optimized forhuman voice by searching a range 62 Hz-1284 Hz, and frequencies outsidethis range are not considered as potential matches. This optimizationspeeds up computation significantly.

In the flowchart shown in FIG. 7, the above-described qualifying processis referenced at 708 where, locally maximal bins in each column 802 areidentified, after which a Gaussian distribution is fit to the data inthe nearest five bins 804 relative to the locally maximal bin (two lowerbins, the center bin which is the local maxima, and two higher bins).This fitting process allows the contours of the local maxima (e.g.,local maxima 814, 816, 818) to be characterized. According to oneaspect, the Gaussian distribution can be fit to the data by using themaximum likelihood method. This fitting process is applied in this wayas a basis to estimate and/or characterize the shape and amplitude ofthe peak. The power level p_(i), standard deviation σi, and meanfrequency of that Gaussian distribution is recorded in a peak candidatelist.

At 710 the process continues by correcting octave errors. As usedherein, the term “octave error” refers to errors in pitch identificationwhich are off by approximately one octave (known as a partial onset).When attempting to identify a pitch, conventional algorithms will oftensimply choose the pitch candidate having the greatest magnitude powerlevel. But as may be appreciated from the plot in FIG. 8, a pitchcandidate represented by a bin with the greatest magnitude of signalpower does not always correspond to the true pitch in associated with anaudio signal sample. Early-onset (lower than true pitch) and harmonic(higher than true pitch) partial onsets often accompany the voice of asinger due to the complexity of the human vocal system. These types ofoctave errors manifest themselves as the groups of bins (e.g. bin groups808, 810) which appear below and above bins 806 in FIG. 8A. Conventionalalgorithms that do not account for these effects are prone to makingoctave errors. In this regard it may be noted that the term “octaveerror” is in some respects a misnomer, since early-onset and harmonicpitches are not in general off by precisely an octave. However, itremains a useful shorthand way of referring to pitch detection errorswhich are generally off by approximately one octave.

Octave errors in the solution presented herein are advantageouslycorrected using the following corrective function to re-score each binassociated with an FFT of a particular audio sample:

$p_{i}^{*} = {p_{i} + {\sum\limits_{\omega \in \Omega}{\int_{x \in G_{i}}{{{Q_{A}( {\omega - x} )} \cdot {G_{i}(x)}}{dx}}}}}$

-   where-   p_(i), is the power of the i-th peak candidate,-   Ω_(iϵN)=69.203 ln(i) is a discrete set of partial onsets unique to    human vocal system,-   ω is a partial onset frequency in Ω-   G_(i) is the Gaussian distribution fit to the i-th peak candidate,-   x is a frequency offset in the domain of Ω, and-   Q_(A) is the constant-Q transform of the audio signal.-   Conceptually, this equation adds additional energy to each peak    candidate when energy distribution or shape of the peak within the    partial onsets of that pitch look similar to the energy distribution    of the peak candidate under consideration. An example of such a    scenario is illustrated within column 802 of FIG. 8, wherein a    partial onset peak present in bin group 808 and/or 810 might look    similar to a peak candidate bin in bin group 806. Note that this    corrective function presented herein only makes sense in the    “constant Q” domain and not after a traditional FFT. This correction    factor is effective at removing spurious pitch candidates without    partial onset support (i.e. unlikely to be due to human singing) and    reinforces the true pitch. At 712 the RTPD will determine the true    pitch of each audio sample by selecting the pitch with largest    p*_(i) value from among the bins associated with that particular    audio sample.

At 714 and 716 the RTPD processing algorithm removes common pitchtracking errors that occur during the beginning of sung notes and aroundharsh consonants where no true pitch exists. It achieves this result byusing a bilateral filter to remove outlier detected pitches and byautomatically filling holes or gaps where no pitch could be detected. Asis known, a bilateral filter uses two kernels simultaneously. Forpurposes of the present solution, the spatial kernel is a Gaussianoperating on the pitch frequency, and the range kernel is a Gaussianoperating on the audio signal amplitude. This can also be thought of asa weighted average at each audio sample, where the weights aredetermined based on similarity in both pitch and amplitude. Afteroutlier pitch values are removed, the result can be displayed at 718.Thereafter, the process can terminate at 720 or continue with otherprocessing.

Note Transcription Algorithm

The solution disclosed herein also concerns a process involving notetranscription. This note transcription process is illustrated in FIGS.9A and 9B. The process utilizes a note transcription (NT) algorithm. TheNT algorithm is an audio analysis procedure for converting an audiorecording of an individual singer (monophonic audio) to a sequence ofnon-overlapping notes with pitch and duration information (suitable forcreating sheet music or a MIDI file, for example). While RTPD isoptimized for real-time feedback and provides information on the pitchhundreds of times per second, NT is optimized for detecting individualnotes aligned to the time signature and tempo of a song. In practice, NTmust often make difficult decisions when singers are slightly betweentwo keys (C vs C# for example), which necessitates deeper contextualawareness than RTPD. Utilization of two entirely different algorithms inthis way is noteworthy in this context. By selectively applying adifferent audio analysis process or method to what might otherwise beunderstood to be basically the same problem, a non-obvious result isobtained for achieving improvements in both real-time pitch detectionand in note transcription. These performance enhancements would not bepossible one algorithm or the other was used exclusively for both typesof audio analysis.

To facilitate creation of a new harmony, it is advantageous to have (1)the audio of the singer for each harmony track (independently) and (2)some user-specified timing information applicable of the “sheet music”(which can include one or more of the time signature, tempo, and notes)over the duration of the song. Accordingly, the process 900 can begin at902 and continue at 904 where the system receives a user input manuallyspecifying a time signature and tempo which is applicable to atranscribed sheet music.

Conventional note transcription solutions do not accept the song tempoas input, since this information is not available in a typicalunstructured setting. In contrast, the NT algorithm is intended to beembedded into an application where this information can be provided bythe user before a particular song is sung. Since the time signature andtempo are set in advance, the search space for possible noteconfigurations is greatly reduced. Consequently, the NT algorithm isable to improve the accuracy and fidelity of transcription results overconventional note transcription solutions.

After the user has manually entered the time signature and tempo data,the user can sing and record each part or track of the harmony at 906.Thereafter, the process can continue whereby the one or more audiotracks associated with the user singing is automatically analyzed by theNT algorithm. As explained below in greater detail, the result is anautomatic production or transcription of the corresponding “sheetmusic”, which can later be edited.

According to one aspect, the NT algorithm can solve the transcriptionproblem by converting the problem of audio transcription into ashortest-path problem. This process is best understood with reference toFIG. 10, which is a simplified diagram showing a shortest path throughthe network that defines the optimal pitch assignment for each note. Ingraph theory, the shortest path problem involves finding a path betweentwo vertices (or nodes) in a graph such that the sum of the weights ofits constituent edges is minimized. In the present solution, each column1004 of nodes in a graph 1000 represents one eighth note duration in thesong, and individual nodes 1002 will represent a possible pitch for thiseighth note.

The weight assigned to each edge 1006 between adjacent nodes i and j isthe negative log likelihood of the probability that the next note hasthe pitch associated with node j, assuming that node i was selected.This edge weight balances the probability that node j is in the songindependent of any other information, along with a regularization factorthat takes into account the previous note. The regularization isimportant in cases, for example, where a singer is slightly off pitch oroscillating between notes; in this case, even though a note may appearto be the most likely in isolation, making a global decision based onthe entire path through the notes via the regularization factor allowsus to choose one continuous pitch for several notes in a row rather thanoscillate incorrectly between notes because each is slightly more likelyin isolation. The shortest path from source s to sink t will include onenode per column 1004, identifying which pitch should be assigned to theeighth note.

There are several steps involved with the assigning weights to each edge1006 of the graph. The process first segments at 908 the entire audiosignal A into fractional notes or note segments based on the currentknown tempo: {A₀,A₁,A₂ , . . . }. In some scenarios described herein,the fractional notes or note segments can comprise eighth notes. Thespecific choice of eighth notes is arbitrary, and not consideredcritical to the solution. Accordingly, other note durations are alsopossible. Still, for the purposes of the presenting the solution herein,it is convenient to describe the process with respect to eighth notes.Each eighth note of audio is then subsampled at 910 into multiplestandard length (potentially overlapping) “chunks” {c₀, c₁ , . . . ,c_(M)}ϵA_(i),. In a solution presented herein, these chunks can comprise4096 subsamples (each 93 ms in duration for 44.1 kHz audio, and 85 ms induration for 48 kHz audio) and eight chunks per note are selected.However, the solution is not limited in this regard and subsamples ofdifferent durations and alternative note segmentations are alsopossible.

The chunks of audio signal obtained at 910 can either be sampledregularly from the eighth note of audio, or sampled at randomized timeswithin the eighth note. According to one scenario, the chunks arerandomly sampled from among a distribution that samples more frequentlynear the middle of the eighth note. Such a scenario can help avoid dataobtained near the leading and trailing edges of notes that might containsamples of previous/next notes when the singer is not perfectly ontempo.

After the chunks have been obtained at 910, the process continues bycomputing at 912 the autocorrelation R (τ) of each chunk with itself,normalized by the size of the overlapping domain, to detect theprobability of each frequency per chunk. This process can be understoodwith reference to the following expression:

${R(\tau)} = {\sum\limits_{i = 0}^{4096}\frac{( {{c( {i + \tau} )} - {c(i)}} )^{2}}{4096 - \tau}}$

-   where-   τ is the lag time in samples,-   c (i) is the i^(th) sample from audio in the chunk, and-   c(i+τ) is a sample from the same chunk, offset by the time lag.-   R(τ)=0 for an audio signal perfectly periodic every x samples. The    frequency probability is defined as S(f)=R(⁴⁰⁹⁶/f)⁻¹.

An example result of the foregoing autocorrelation process is presentedin FIG. 11. FIG. 11 is a graph showing sample index plus frequencyprobability value (y axis), plotted against musical note values (x axis)for a small selection of chunks. In FIG. 11, each line 1102 correspondsto one chunk and the peaks (e.g., peaks 1104 a, 1104 b) in each lineindicate a local probability maxima corresponding to certain frequencieslisted along the frequency axis. For example, it can be observed in FIG.10 that local maxima 1104 a corresponds approximately to note E2, andlocal maxima 1104 b corresponds approximately to note B2. In thisregard, FIG. 11 is useful for illustrating one difficulty withautocorrelation-based methods for evaluating frequency probability. Inparticular, it can be observed that for any given chunk or line, a localprobability maxima is often detected at multiples of the true notefrequency.

The occurrence of multiple local probability maxima as shown in FIG. 11can be understood as true octave errors, and are due to the periodicnature of the audio signal. To correct for this problem and in order toassign the appropriate probabilities, process 900 continues at 914 byfurther processing each monotonically increasing local maxima in orderfrom lowest to highest frequency {0,f₀, f₁ , . . . , f_(N)}, andassigning them probabilities. Probabilities are advantageously assignedby making use of a mathematical process that gives preference to theoriginal lowest frequency when the probabilities are similar, withoutover-penalizing frequencies that may have a peak undertone as long asthe probability is significantly lower. An example of such a process isthe integral of a Kumaraswamy distribution over the probability range,{0,f₀,f₁ , . . . , f_(N)}, as follows:

P(f _(i))=∫_(x=f) _(i−1) ^(f) ^(i) 10x(1−x ²)⁴

These probabilities are then quantized at 916 to their nearest notes onthe western musical scale. For example, in some scenarios thisquantization can be accomplished by linearly distributing theprobability to the nearest note centers.

Another difficulty when detecting pitch in human singing is vibrato(notes sung with significantly oscillating pitch). Such vibrato typesinging diffuses the note probability between adjacent notes, and cancause a single long note to appear as many oscillating nearby notes. TheNT algorithm in process 900 can correct for this potential error at 918.The vibrato correction involves introducing a regularization factor tothe edge weights 906, equal to a Laplace distribution

${(x)} = {e{\frac{x}{4}.}}$

At 920, to compute the final edge weights 906 between any two adjacentnodes i and j 902, we use every chunk from the j-th eighth note, andaverage the negative log likelihood of that node's pitch being thecorrect pitch for this note:

${E( {i,j} )} = {\frac{1}{M}{\sum\limits_{M}{- {\log ( {{( {f_{i} - f_{j}} )}*{P( f_{j} )}} )}}}}$

-   where-   i, j indicate two nodes in the graph,-   M is the number of chunks,-   l(f_(i)-f_(j)) is the Laplace distribution applied to the difference    in frequency of the nodes,-   P (f_(i)) is the note probability indicated above.-   Edge weights connected to the source or sink node are not important    in this case (all set to 1). Intuitively, the edge weight equation    balances the probability of each note in isolation based on just the    eighth note's audio signal, with a regularization factor that    prefers long uninterrupted notes over many quick back and forth    notes, which is important in cases where a singer may be off-pitch    and halfway between two options, or in cases of vibrato.

The process continues at 922 by inserting one or more “virtual note”nodes in the graph to represent periods of silence or the lack of anysung note. In other words, each eighth note can be assigned f=ø toindicate periods of silence or a lack of any actual note. The edgeweight leading to each silent node is set to:

${E( {i,s} )} = {\frac{1}{M}{\sum\limits_{M}( {1 - \frac{\overset{\_}{c}}{10\overset{\_}{A}}} )}}$

-   where-   i is any node and s is the silent “virtual note” node,-   M is the number of chunks,-   c is the average intensity of the audio signal in one chunk, and-   Ā is the average intensity in the audio signal from the entire song.-   This sets the edge weight based on how loud a chunk is compared to    the average song loudness (quiet chunks are more likely to indicate    a silent note). Note also that no vibrato correction is applied for    silent notes.

At 924 the process continues by constructing the graph using for eachnode the musical notes identified in steps 908-922. The shortest paththrough this graph can be solved efficiently using conventional methodsat 926. This solution will represent the optimal (highest probability)note selection, since the minimum sum of edge weights (i.e. shortestpath) equals the highest product of probabilities:

min (Σ−log (P))=−Σmax(log(P))=−log(max(ΠP))

The path determined in 926 can be converted at 928 into a sequence ofeighth notes. According to one aspect, adjacent notes with the samepitch can be merged at 930 into larger notes (quarter, half notes, andso on). However, other solutions are also possible and it may be usefulto consider changes at the edges of notes to determine whether to mergenotes or leave them separated. The note transcription process can thenterminate at 932 or can continue with other processing. The transcribednote data resulting from the process can be imported into theapplication along with the recorded singing track.

The systems described herein can comprise one or more components such asa processor, an application specific circuit, a programmable logicdevice, a digital signal processor, or other circuit programmed toperform the functions described herein. Embodiments can be realized inone computer system or several interconnected computer systems. Any kindof computer system or other apparatus adapted for carrying out themethods described herein is suited. A typical combination of hardwareand software can be a general-purpose computer system. Thegeneral-purpose computer system can have a computer program that cancontrol the computer system such that it carries out the methodsdescribed herein.

Embodiments of the inventive arrangements disclosed herein can berealized in one computer system. Alternative embodiments can be realizedin several interconnected computer systems. Any kind of computer systemor other apparatus adapted for carrying out the methods described hereinis suited. A typical combination of hardware and software can be ageneral-purpose computer system. The general-purpose computer system canhave a computer program that can control the computer system such thatit carries out the methods described herein. A computer system asreferenced herein can comprise various types of computing systems anddevices, including a server computer, a personal computer (PC), a laptopcomputer, a desktop computer, a network router, switch or bridge, or anyother device capable of executing a set of instructions (sequential orotherwise) that specifies actions to be taken by that device. In somescenarios, the user equipment can comprise a portable data communicationdevice such as a smart phone, a tablet computer, or a laptop computer.

Referring now to FIG. 12, there is shown a hardware block diagramcomprising a computer system 1200. The machine can include a set ofinstructions which are used to cause the computer system to perform anyone or more of the methodologies discussed herein. In a networkeddeployment, the machine can function as a server, such as applicationserver 102. In some scenarios, the exemplary computer system 1200 cancorrespond to each of the user equipment computer systems 106 ₁-106_(n). In some embodiments, the computer 1200 can operate independentlyas a standalone device. However, embodiments are not limited in thisregard and in other scenarios the computer system can be operativelyconnected (networked) to other machines in a distributed environment tofacilitate certain operations described herein. Accordingly, while onlya single machine is illustrated it should be understood that embodimentscan be taken to involve any collection of machines that individually orjointly execute one or more sets of instructions as described herein.

The computer system 1200 is comprised of a processor 1202 (e.g. acentral processing unit or CPU), a main memory 1204, a static memory1206, a drive unit 1208 for mass data storage and comprised of machinereadable media 1220, input/output devices 1210, a display unit 1212(e.g. a liquid crystal display (LCD), a solid state display, or acathode ray tube (CRT)), and a network interface device 1214.Communications among these various components can be facilitated bymeans of a data bus 1218. One or more sets of instructions 1224 can bestored completely or partially in one or more of the main memory 1204,static memory 1206, and drive unit 1208. The instructions can alsoreside within the processor 1202 during execution thereof by thecomputer system. The input/output devices 1210 can include a keyboard, amouse, a multi-touch surface (e.g. a touchscreen). The input/outputdevices can also include audio components such as microphones,loudspeakers, audio output jacks, and so on. The network interfacedevice 1214 can be comprised of hardware components and software orfirmware to facilitate wired or wireless network data communications inaccordance with a network communication protocol utilized by a datanetwork 100, 200.

The drive unit 1208 can comprise a machine readable medium 1220 on whichis stored one or more sets of instructions 1224 (e.g. software) whichare used to facilitate one or more of the methodologies and functionsdescribed herein. The term “machine-readable medium” shall be understoodto include any tangible medium that is capable of storing instructionsor data structures which facilitate any one or more of the methodologiesof the present disclosure. Exemplary machine-readable media can includemagnetic media, solid-state memories, optical-media and so on. Moreparticularly, tangible media as described herein can include; magneticdisks; magneto-optical disks; CD-ROM disks and DVD-ROM disks,semiconductor memory devices, electrically erasable programmableread-only memory (EEPROM)) and flash memory devices. A tangible mediumas described herein is one that is non-transitory insofar as it does notinvolve a propagating signal.

Embodiments disclosed herein can advantageously make use of well-knownlibrary functions such as OpenAL or AudioKit to facilitate reading andwriting of MP3 files, and for handling audio input/output functions.These audio input/output functions can include for example microphoneand speaker connectivity, volume adjustments, wireless networkingfunctionality and so on).

Computer system 1200 should be understood to be one possible example ofa computer system which can be used in connection with the variousembodiments. However, the embodiments are not limited in this regard andany other suitable computer system architecture can also be used withoutlimitation. Dedicated hardware implementations including, but notlimited to, application-specific integrated circuits, programmable logicarrays, and other hardware devices can likewise be constructed toimplement the methods described herein. Applications that can includethe apparatus and systems of various embodiments broadly include avariety of electronic and computer systems. Some embodiments mayimplement functions in two or more specific interconnected hardwaremodules or devices with related control and data signals communicatedbetween and through the modules, or as portions of anapplication-specific integrated circuit. Thus, the exemplary system isapplicable to software, firmware, and hardware implementations.

Further, it should be understood that embodiments can take the form of acomputer program product on a tangible computer-usable storage medium(for example, a hard disk or a CD-ROM). The computer-usable storagemedium can have computer-usable program code embodied in the medium. Theterm computer program product, as used herein, refers to a devicecomprised of all the features enabling the implementation of the methodsdescribed herein. Computer program, software application, computersoftware routine, and/or other variants of these terms, in the presentcontext, mean any expression, in any language, code, or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: a) conversion to anotherlanguage, code, or notation; or b) reproduction in a different materialform.

The MHT described herein advantageously combines qualitativeself-assessment through hearing singer's voice with objective assessmentthrough visualization of pitch, dynamics, and rhythm. It shows targetnotes and actual dynamics in an innovative way. It further allows forcollaborative, asynchronous, virtual harmonizing via recording and tracksharing. Multiple tracks of different formats can be synced withrecorded tracks to allow seamless practicing in several modes andinstant feedback. A pitch of each note that has been sung and theamplitude of each note can be updated at a rate which is selected toprovide a smooth and accurate user experience with respect to notetracking and feedback. For example, in some scenarios, note updates canoccur at a rate of every 18 ms (60 Hz) or 33 ms (30 Hz). Of course thesevalues are merely provided as examples and are not intended to limit therange of note update frequency. The display format facilitatesvisualization of many aspects of the voice, with a particularlyinnovative treatment of the voice's amplitude (volume), a designchallenge met with a volume meter (a perpendicular bar that runshorizontally along a note, with the left being lowest volume and theright being loudest).

The described features, advantages and characteristics of the varioussolutions disclosed herein can be combined in any suitable manner. Oneskilled in the relevant art will recognize, in light of the descriptionherein, that the disclosed systems, devices and/or methods can bepracticed without one or more of the specific features. In otherinstances, additional features and advantages may be recognized incertain scenarios that may not be present in all instances.

As used in this document, the singular form “a”, “an”, and “the” includeplural references unless the context clearly dictates otherwise. Unlessdefined otherwise, all technical and scientific terms used herein havethe same meanings as commonly understood by one of ordinary skill in theart. As used in this document, the term “comprising” means “including,but not limited to”.

Although the various solutions have been illustrated and described withrespect to one or more implementations, equivalent alterations andmodifications will occur to others skilled in the art upon the readingand understanding of this specification and the annexed drawings. Inaddition, while a particular feature may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.Thus, the breadth and scope of the disclosure herein should not belimited by any of the above descriptions. Rather, the scope of theinvention should be defined in accordance with the following claims andtheir equivalents.

We claim:
 1. A method for accurate transcription of voiced musicalnotes, comprising: receiving a user specified timing information for amusical composition to be transcribed; using a microphone to convertsound waves comprised of monophonic audio produced by a voiced renditionof the musical composition, into an electronic audio signal; storing theelectronic audio signal in a data storage device; processing theelectronic audio signal in an electronic processing device to determinea true pitch of each musical note of the voiced rendition; and whereinthe true pitch is determined in the electronic processing device bysegmenting the electronic audio signal into a plurality of musical notesegments; sampling each note segment to obtain a plurality of audiosamples; for each audio sample, computing an autocorrelation todetermine a probability value of each audio frequency contained withinthe audio sample; identifying at least one local maxima in the energyassociated with one or more audio frequencies comprising the audiosample; applying a corrective function to the eliminate one or more ofthe local maxima which are determined to be associated with octaveerrors; computing edge weights of a plurality of adjacent nodes i, jcomprising a graph, where each node represents one of the musical notesegments, and where the edge weight between adjacent nodes i and j is anegative log likelihood of the probability that a next musical notesegment has a pitch associated with node j, assuming that node i wasselected; and determining a shortest-path through the graph to selectthe true pitch to be assigned each of the musical note segments.
 2. Themethod according to claim 1, further comprising selectivelyconcatenating contiguous groups of the musical note segments which aredetermined to have the same pitch into notes of longer duration.
 3. Themethod according to claim 1, further comprising storing transcriptiondata which specifies a true pitch of each of the musical note in thevoiced rendition.
 4. The method according to claim 1, wherein each ofthe musical note segments is selected to comprise an eighth note.
 5. Themethod according to claim 1, wherein the audio samples arepreferentially selected from portions of the note segment which excludeleading and trailing edges of the note segment
 6. The method accordingto claim 1, wherein the user specified timing information is selectedfrom the group consisting of a music time signature and a tempoassociated with the musical composition.
 7. The method according toclaim 1, wherein the corrective function reduces the occurrence ofoctave errors by giving preference to a first audio frequency associatedwith a first local maxima having the lowest audio frequency of the oneor more local maxima, under conditions where others of the one or morelocal maxima associated with the same audio sample have a probabilityvalue magnitude within a predetermined threshold range of the firstlocal maxima.
 8. The method according to claim 7, further comprising foreach audio sample, quantizing the audio frequency associated with theone or more local maxima to a nearest musical note on the westernmusical scale.
 9. The method according to claim 8, wherein the edgeweights between any two adjacent nodes i, j are computed by averagingthe negative log likelihood of the probability values which wereassigned to the audio frequency associated with each musical samplebased on the local maxima.
 10. The method according to claim 9, furthercomprising applying a regularization function when assigning the edgeweights to minimize a possibility that an oscillating pitch associatedwith vibrato singing will be transcribed as many short oscillating noteas opposed to one continuous note.
 11. The method according to claim 10,wherein the regularization function is comprised of a Laplacedistribution.
 12. The method according to claim 1, wherein the musicalnote segments represented by nodes in the graph include one or morevirtual notes which indicate periods of silence or absence of any actualnote.
 13. The method according to claim 1, wherein the voiced renditioncomprises a first musical harmony part of the musical composition, andthe method further comprises causing the electronic processing device todisplay on a note grid the musical notes which correspond to the truepitch which has been determined.
 14. The method according to claim 13,wherein the musical composition is further comprised of at least asecond musical harmony part, and wherein the electronic processingdevice is caused to further display on the note grid concurrent with themusical notes comprising the first musical harmony part, the musicalnotes which are associated with at least the second musical harmonypart.
 15. The method according to claim 13, further comprisingdetermining in real-time a pitch of a second voiced rendition of thefirst musical harmony part.
 16. The method according to claim 15,further comprising displaying a pitch indicator in conjunction with thenote grid to indicate whether musical notes of the second voicedrendition match a specified pitch and timing of the musical notes of thefirst musical harmony part.
 17. The method according to claim 15,wherein the second musical harmony part is caused by the electronicprocessing device to be audibly played through a loudspeaker concurrentwith determining the pitch of the second voiced rendition of the firstharmony part.
 18. The method according to claim 15, wherein an audioanalysis process applied for the real-time determining of the pitch ofthe second voiced rendition is different from the audio analysis processapplied for determining the true pitch of the voiced rendition.